Credit Card Fraud Detection Model¶

In [33]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
In [34]:
from dataprep.eda import*
In [35]:
df=pd.read_csv("creditcard.csv")

Getting Knowledge about the dataset¶

In [36]:
df.head()
Out[36]:
Time V1 V2 V3 V4 V5 V6 V7 V8 V9 ... V21 V22 V23 V24 V25 V26 V27 V28 Amount Class
0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 149.62 0
1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 2.69 0
2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0
3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 123.50 0
4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 69.99 0

5 rows × 31 columns

In [37]:
df.shape
Out[37]:
(284807, 31)
In [38]:
plot(df)

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Out[38]:
DataPrep.EDA Report
Dataset Statistics
Number of Variables 31
Number of Rows 284807
Missing Cells 0
Missing Cells (%) 0.0%
Duplicate Rows 1081
Duplicate Rows (%) 0.4%
Total Size in Memory 67.4 MB
Average Row Size in Memory 248.0 B
Variable Types
  • Numerical: 30
  • Categorical: 1
Dataset Insights
V2 and V5 have similar distributions Similar Distribution
V2 and V11 have similar distributions Similar Distribution
V2 and V13 have similar distributions Similar Distribution
V5 and V9 have similar distributions Similar Distribution
V5 and V11 have similar distributions Similar Distribution
V5 and V13 have similar distributions Similar Distribution
V5 and V15 have similar distributions Similar Distribution
V7 and V13 have similar distributions Similar Distribution
V7 and V15 have similar distributions Similar Distribution
V7 and V16 have similar distributions Similar Distribution
Dataset Insights
V7 and V18 have similar distributions Similar Distribution
V7 and V19 have similar distributions Similar Distribution
V7 and V22 have similar distributions Similar Distribution
V9 and V10 have similar distributions Similar Distribution
V13 and V15 have similar distributions Similar Distribution
V14 and V16 have similar distributions Similar Distribution
V14 and V18 have similar distributions Similar Distribution
V14 and V19 have similar distributions Similar Distribution
V15 and V16 have similar distributions Similar Distribution
V16 and V18 have similar distributions Similar Distribution
Dataset Insights
V16 and V19 have similar distributions Similar Distribution
V18 and V19 have similar distributions Similar Distribution
V18 and V22 have similar distributions Similar Distribution
V19 and V22 have similar distributions Similar Distribution
V20 and V21 have similar distributions Similar Distribution
V1 is skewed Skewed
V2 is skewed Skewed
V3 is skewed Skewed
V4 is skewed Skewed
V5 is skewed Skewed
Dataset Insights
V6 is skewed Skewed
V7 is skewed Skewed
V8 is skewed Skewed
V9 is skewed Skewed
V10 is skewed Skewed
V12 is skewed Skewed
V14 is skewed Skewed
V16 is skewed Skewed
V17 is skewed Skewed
V18 is skewed Skewed
Dataset Insights
V19 is skewed Skewed
V20 is skewed Skewed
V21 is skewed Skewed
V22 is skewed Skewed
V23 is skewed Skewed
V24 is skewed Skewed
V25 is skewed Skewed
V27 is skewed Skewed
V28 is skewed Skewed
Amount is skewed Skewed
Dataset Insights
Class has constant length 1 Constant Length
V1 has 141456 (49.67%) negatives Negatives
V2 has 134218 (47.13%) negatives Negatives
V3 has 128163 (45.0%) negatives Negatives
V4 has 144105 (50.6%) negatives Negatives
V5 has 148928 (52.29%) negatives Negatives
V6 has 176633 (62.02%) negatives Negatives
V7 has 135852 (47.7%) negatives Negatives
V8 has 135521 (47.58%) negatives Negatives
V9 has 149455 (52.48%) negatives Negatives
Dataset Insights
V10 has 159922 (56.15%) negatives Negatives
V11 has 145632 (51.13%) negatives Negatives
V12 has 121089 (42.52%) negatives Negatives
V13 has 144123 (50.6%) negatives Negatives
V14 has 133762 (46.97%) negatives Negatives
V15 has 136234 (47.83%) negatives Negatives
V16 has 132552 (46.54%) negatives Negatives
V17 has 153635 (53.94%) negatives Negatives
V18 has 142942 (50.19%) negatives Negatives
V19 has 141804 (49.79%) negatives Negatives
Dataset Insights
V20 has 169130 (59.38%) negatives Negatives
V21 has 153196 (53.79%) negatives Negatives
V22 has 141402 (49.65%) negatives Negatives
V23 has 148400 (52.11%) negatives Negatives
V24 has 127732 (44.85%) negatives Negatives
V25 has 139302 (48.91%) negatives Negatives
V26 has 151720 (53.27%) negatives Negatives
V27 has 141004 (49.51%) negatives Negatives
V28 has 125888 (44.2%) negatives Negatives
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8

Number of plots per page:

Time
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
V1
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V1 is skewed
  2. V1 has 141456 (49.67%) negatives
V2
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V2 is skewed
  2. V2 has 134218 (47.13%) negatives
V3
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V3 is skewed
  2. V3 has 128163 (45.0%) negatives
V4
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V4 is skewed
  2. V4 has 144105 (50.6%) negatives
V5
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V5 is skewed
  2. V5 has 148928 (52.29%) negatives
V6
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V6 is skewed
  2. V6 has 176633 (62.02%) negatives
V7
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V7 is skewed
  2. V7 has 135852 (47.7%) negatives
V8
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V8 is skewed
  2. V8 has 135521 (47.58%) negatives
V9
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V9 is skewed
  2. V9 has 149455 (52.48%) negatives
V10
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V10 is skewed
  2. V10 has 159922 (56.15%) negatives
V11
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V11 has 145632 (51.13%) negatives
V12
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V12 is skewed
  2. V12 has 121089 (42.52%) negatives
V13
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V13 has 144123 (50.6%) negatives
V14
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V14 is skewed
  2. V14 has 133762 (46.97%) negatives
V15
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V15 has 136234 (47.83%) negatives
V16
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V16 is skewed
  2. V16 has 132552 (46.54%) negatives
V17
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V17 is skewed
  2. V17 has 153635 (53.94%) negatives
V18
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V18 is skewed
  2. V18 has 142942 (50.19%) negatives
V19
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V19 is skewed
  2. V19 has 141804 (49.79%) negatives
V20
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V20 is skewed
  2. V20 has 169130 (59.38%) negatives
V21
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V21 is skewed
  2. V21 has 153196 (53.79%) negatives
V22
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V22 is skewed
  2. V22 has 141402 (49.65%) negatives
V23
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V23 is skewed
  2. V23 has 148400 (52.11%) negatives
V24
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V24 is skewed
  2. V24 has 127732 (44.85%) negatives
V25
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V25 is skewed
  2. V25 has 139302 (48.91%) negatives
V26
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V26 has 151720 (53.27%) negatives
V27
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V27 is skewed
  2. V27 has 141004 (49.51%) negatives
V28
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. V28 is skewed
  2. V28 has 125888 (44.2%) negatives
Amount
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. Amount is skewed
Class
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
Column Insights
  1. Class has constant length 1
Prev1234Next
In [39]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 284807 entries, 0 to 284806
Data columns (total 31 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   Time    284807 non-null  float64
 1   V1      284807 non-null  float64
 2   V2      284807 non-null  float64
 3   V3      284807 non-null  float64
 4   V4      284807 non-null  float64
 5   V5      284807 non-null  float64
 6   V6      284807 non-null  float64
 7   V7      284807 non-null  float64
 8   V8      284807 non-null  float64
 9   V9      284807 non-null  float64
 10  V10     284807 non-null  float64
 11  V11     284807 non-null  float64
 12  V12     284807 non-null  float64
 13  V13     284807 non-null  float64
 14  V14     284807 non-null  float64
 15  V15     284807 non-null  float64
 16  V16     284807 non-null  float64
 17  V17     284807 non-null  float64
 18  V18     284807 non-null  float64
 19  V19     284807 non-null  float64
 20  V20     284807 non-null  float64
 21  V21     284807 non-null  float64
 22  V22     284807 non-null  float64
 23  V23     284807 non-null  float64
 24  V24     284807 non-null  float64
 25  V25     284807 non-null  float64
 26  V26     284807 non-null  float64
 27  V27     284807 non-null  float64
 28  V28     284807 non-null  float64
 29  Amount  284807 non-null  float64
 30  Class   284807 non-null  int64  
dtypes: float64(30), int64(1)
memory usage: 67.4 MB

Preprocessing¶

In [40]:
#check how many null values for each column

print(df.isna().sum())

Time      0
V1        0
V2        0
V3        0
V4        0
V5        0
V6        0
V7        0
V8        0
V9        0
V10       0
V11       0
V12       0
V13       0
V14       0
V15       0
V16       0
V17       0
V18       0
V19       0
V20       0
V21       0
V22       0
V23       0
V24       0
V25       0
V26       0
V27       0
V28       0
Amount    0
Class     0
dtype: int64
In [41]:
#Check number of duplicate values

df.duplicated().sum()
Out[41]:
1081
In [42]:
#Remove duplicate values

df=df.drop_duplicates(keep='first')
In [43]:
df.duplicated().sum()
Out[43]:
0

Getting knowledge about the range of the data columns¶

In [76]:
df_minimum=df.min(axis=0)
In [77]:
df_maximum=df.max(axis=0)
In [78]:
df_minimum
Out[78]:
Time        0.000000
V1        -56.407510
V2        -72.715728
V3        -48.325589
V4         -5.683171
V5       -113.743307
V6        -26.160506
V7        -43.557242
V8        -73.216718
V9        -13.434066
V10       -24.588262
V11        -4.797473
V12       -18.683715
V13        -5.791881
V14       -19.214325
V15        -4.498945
V16       -14.129855
V17       -25.162799
V18        -9.498746
V19        -7.213527
V20       -54.497720
V21       -34.830382
V22       -10.933144
V23       -44.807735
V24        -2.836627
V25       -10.295397
V26        -2.604551
V27       -22.565679
V28       -15.430084
Amount      0.000000
Class       0.000000
dtype: float64
In [79]:
df_minmax=df_minimum.to_frame()
In [80]:
#rename column name

df_minmax= df_minmax.rename (columns = {0:'Minimum'})
In [81]:
df_minmax
Out[81]:
Minimum
Time 0.000000
V1 -56.407510
V2 -72.715728
V3 -48.325589
V4 -5.683171
V5 -113.743307
V6 -26.160506
V7 -43.557242
V8 -73.216718
V9 -13.434066
V10 -24.588262
V11 -4.797473
V12 -18.683715
V13 -5.791881
V14 -19.214325
V15 -4.498945
V16 -14.129855
V17 -25.162799
V18 -9.498746
V19 -7.213527
V20 -54.497720
V21 -34.830382
V22 -10.933144
V23 -44.807735
V24 -2.836627
V25 -10.295397
V26 -2.604551
V27 -22.565679
V28 -15.430084
Amount 0.000000
Class 0.000000
In [82]:
#add maximum column 
df_minmax['Maximum']=df_maximum
In [83]:
df_minmax
Out[83]:
Minimum Maximum
Time 0.000000 172792.000000
V1 -56.407510 2.454930
V2 -72.715728 22.057729
V3 -48.325589 9.382558
V4 -5.683171 16.875344
V5 -113.743307 34.801666
V6 -26.160506 73.301626
V7 -43.557242 120.589494
V8 -73.216718 20.007208
V9 -13.434066 15.594995
V10 -24.588262 23.745136
V11 -4.797473 12.018913
V12 -18.683715 7.848392
V13 -5.791881 7.126883
V14 -19.214325 10.526766
V15 -4.498945 8.877742
V16 -14.129855 17.315112
V17 -25.162799 9.253526
V18 -9.498746 5.041069
V19 -7.213527 5.591971
V20 -54.497720 39.420904
V21 -34.830382 27.202839
V22 -10.933144 10.503090
V23 -44.807735 22.528412
V24 -2.836627 4.584549
V25 -10.295397 7.519589
V26 -2.604551 3.517346
V27 -22.565679 31.612198
V28 -15.430084 33.847808
Amount 0.000000 25691.160000
Class 0.000000 1.000000
In [84]:
df.drop(["Time","Amount"], axis=1, inplace=True)

Scalling Dataset¶

In [85]:
from sklearn.preprocessing import StandardScaler
In [87]:
X=df.iloc[:,:-1].values
Y=df.iloc[:,-1].values
In [88]:
X
Out[88]:
array([[-1.35980713e+00, -7.27811733e-02,  2.53634674e+00, ...,
        -1.89114844e-01,  1.33558377e-01, -2.10530535e-02],
       [ 1.19185711e+00,  2.66150712e-01,  1.66480113e-01, ...,
         1.25894532e-01, -8.98309914e-03,  1.47241692e-02],
       [-1.35835406e+00, -1.34016307e+00,  1.77320934e+00, ...,
        -1.39096572e-01, -5.53527940e-02, -5.97518406e-02],
       ...,
       [ 1.91956501e+00, -3.01253846e-01, -3.24963981e+00, ...,
        -8.73705959e-02,  4.45477214e-03, -2.65608286e-02],
       [-2.40440050e-01,  5.30482513e-01,  7.02510230e-01, ...,
         5.46668462e-01,  1.08820735e-01,  1.04532821e-01],
       [-5.33412522e-01, -1.89733337e-01,  7.03337367e-01, ...,
        -8.18267121e-01, -2.41530880e-03,  1.36489143e-02]])
In [89]:
Y
Out[89]:
array([0, 0, 0, ..., 0, 0, 0], dtype=int64)
In [90]:
#standardization
scale=StandardScaler().fit_transform(X)
In [91]:
scale
Out[91]:
array([[-0.70108232, -0.04168726,  1.68010106, ..., -0.39262206,
         0.33303251, -0.06584955],
       [ 0.60879165,  0.16413764,  0.10927902, ...,  0.26085374,
        -0.0271543 ,  0.0432187 ],
       [-0.7003364 , -0.81133678,  1.17426962, ..., -0.28886092,
        -0.14432548, -0.18382429],
       ...,
       [ 0.98235398, -0.18043304, -2.15503257, ..., -0.18155722,
         0.00680174, -0.08264021],
       [-0.12646526,  0.32465977,  0.46457662, ...,  1.13373436,
         0.27052318,  0.31700384],
       [-0.27686005, -0.1127094 ,  0.46512487, ..., -1.69777619,
        -0.01055821,  0.03994074]])

Splitting the dataset¶

In [92]:
from sklearn.model_selection import train_test_split
In [93]:
x_train, x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2, random_state=100)

Building the model¶

In [94]:
from sklearn.tree import DecisionTreeClassifier
In [95]:
#create decision tree classifier object

clf=DecisionTreeClassifier()
In [96]:
#Train decision tree classifier 

clf=clf.fit(x_train,y_train)
In [97]:
#predict the response for test dataset

y_pred=clf.predict(x_test)
In [98]:
from sklearn import metrics   #for accuracy calculation
In [101]:
print("Accuracy : ", metrics.accuracy_score(y_test,y_pred))

Accuracy :  0.9988721672012124
In [ ]: